AI Engineer
Software Engineering, Data Science
Posted on Aug 5, 2026
AI Engineer – Prompt Engineering & Multi-Agent Systems Opportunity Overview
CornerStone Technology Talent Services is seeking an experienced AI Engineer to design, build, deploy, and optimize production-ready generative AI solutions. This is a hands-on engineering role for someone with deep experience in prompt engineering, AI agent development, and multi-agent orchestration.
The ideal candidate has successfully moved agentic AI solutions beyond proof-of-concept environments and into scalable, reliable deployments. You will develop intelligent agents that can reason, use tools, retrieve enterprise knowledge, coordinate with other agents, and complete complex workflows with appropriate observability, security, and human oversight.
This opportunity is best suited for an engineer who is comfortable owning the full AI solution lifecycle—from prompt and architecture design through deployment, evaluation, monitoring, and continuous improvement.
Key Responsibilities
The successful AI Engineer will be able to demonstrate specific examples of multi-agent solutions they personally designed, developed, deployed, and supported. They should be prepared to explain:
CornerStone Technology Talent Services connects accomplished technology professionals with meaningful opportunities at organizations investing in modern AI and digital transformation. Our recruiting team provides clear communication, thorough preparation, and dedicated support throughout the hiring and onboarding process.
We are committed to presenting opportunities that align with your technical experience, career goals, and long-term professional growth.
CornerStone Technology Talent Services is seeking an experienced AI Engineer to design, build, deploy, and optimize production-ready generative AI solutions. This is a hands-on engineering role for someone with deep experience in prompt engineering, AI agent development, and multi-agent orchestration.
The ideal candidate has successfully moved agentic AI solutions beyond proof-of-concept environments and into scalable, reliable deployments. You will develop intelligent agents that can reason, use tools, retrieve enterprise knowledge, coordinate with other agents, and complete complex workflows with appropriate observability, security, and human oversight.
This opportunity is best suited for an engineer who is comfortable owning the full AI solution lifecycle—from prompt and architecture design through deployment, evaluation, monitoring, and continuous improvement.
Key Responsibilities
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Design, develop, and deploy production-grade AI agents and multi-agent systems.
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Create advanced prompts, system instructions, reusable prompt templates, and structured workflows for large language model applications.
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Develop agents capable of planning, reasoning, tool use, function calling, task delegation, and collaboration with other specialized agents.
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Design agent orchestration patterns, including supervisor-agent, planner-executor, routing, sequential, and parallel workflows.
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Integrate AI agents with enterprise applications, APIs, databases, vector stores, document repositories, and external tools.
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Build retrieval-augmented generation solutions that provide relevant, grounded, and context-aware responses.
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Implement short-term and long-term memory strategies for stateful agent interactions.
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Evaluate model and agent performance using measurable criteria such as accuracy, task completion, response quality, latency, cost, and groundedness.
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Identify and reduce hallucinations, prompt injection risks, unreliable tool execution, and unintended agent behavior.
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Implement guardrails, fallback logic, human-in-the-loop approvals, access controls, and responsible AI practices.
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Build monitoring and observability capabilities for prompts, model responses, agent decisions, tool calls, failures, token consumption, and operational costs.
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Troubleshoot production issues involving prompts, agent workflows, integrations, retrieval quality, and model performance.
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Collaborate with software engineers, data engineers, architects, product leaders, and business stakeholders to translate use cases into scalable AI solutions.
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Document solution architecture, agent behavior, prompt versions, evaluation results, deployment procedures, and operational support requirements.
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Approximately seven or more years of overall software engineering, data engineering, machine learning, or artificial intelligence experience.
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At least three years of relevant hands-on experience developing generative AI, prompt engineering, or AI agent solutions.
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Demonstrated experience building and running multiple AI agents in deployed environments.
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Advanced prompt engineering experience, including system prompts, few-shot prompting, chain-of-thought alternatives, structured output, context management, and prompt optimization.
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Strong understanding of agentic AI concepts such as planning, reasoning, memory, tool use, task routing, agent communication, and workflow orchestration.
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Hands-on experience with large language models and generative AI APIs.
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Strong programming experience with Python and modern API development.
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Experience integrating AI applications with REST APIs, databases, cloud services, and enterprise systems.
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Experience with one or more agent or orchestration frameworks, such as LangChain, LangGraph, Microsoft AutoGen, Semantic Kernel, CrewAI, LlamaIndex, or comparable technologies.
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Experience implementing retrieval-augmented generation using embeddings, vector search, document chunking, retrieval strategies, and grounding techniques.
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Experience deploying AI services in a cloud environment such as Microsoft Azure, Amazon Web Services, or Google Cloud Platform.
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Understanding of model evaluation, prompt testing, agent testing, and production monitoring.
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Strong technical communication skills and the ability to explain complex AI behavior to both technical and nontechnical stakeholders.
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Ability to work effectively in a hybrid environment during U.S. Central Time business hours.
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Experience with Azure OpenAI, Amazon Bedrock, Google Vertex AI, OpenAI APIs, Anthropic models, or other enterprise generative AI platforms.
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Experience building enterprise copilots, autonomous workflow agents, conversational AI systems, or intelligent process-automation solutions.
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Knowledge of Model Context Protocol, function calling, structured outputs, and secure tool integration.
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Experience with vector databases or search platforms such as Azure AI Search, Pinecone, Weaviate, Milvus, pgvector, Elasticsearch, or OpenSearch.
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Familiarity with Docker, Kubernetes, CI/CD pipelines, infrastructure as code, and modern cloud-native deployment practices.
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Experience implementing LLMOps, prompt versioning, model governance, evaluation datasets, tracing, and observability.
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Understanding of AI security, privacy, identity management, data protection, and responsible AI principles.
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Experience balancing solution quality with model latency, token usage, scalability, and operating cost.
The successful AI Engineer will be able to demonstrate specific examples of multi-agent solutions they personally designed, developed, deployed, and supported. They should be prepared to explain:
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The business problem addressed by the agents.
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The responsibilities assigned to each agent.
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How agents communicated, shared context, and delegated work.
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Which models, frameworks, tools, and retrieval methods were used.
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How prompts and agent behavior were evaluated and improved.
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How failures, hallucinations, security risks, and human approvals were handled.
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How the solution was deployed, monitored, and maintained in production.
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Hybrid work environment
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U.S.-based opportunity
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Standard U.S. Central Time business hours
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No routine travel anticipated
CornerStone Technology Talent Services connects accomplished technology professionals with meaningful opportunities at organizations investing in modern AI and digital transformation. Our recruiting team provides clear communication, thorough preparation, and dedicated support throughout the hiring and onboarding process.
We are committed to presenting opportunities that align with your technical experience, career goals, and long-term professional growth.